sábado, julho 22, 2017

“In 2016, nineteen years after my loss to Deep Blue,
the Google-backed AI project DeepMind and its Go-playing offshoot AlphaGo
defeated the world’s top Go player, Lee Sedol. More importantly, as also as
predicted, the methods used to create AlphaGo were more interesting as an IA
Project than anything that had produced the top chess machines. It uses machine
learning and neural networks to teach itself how to play better, as well as
other sophisticated techniques beyond the usual alpha-beta search. Deep Blue
was the end; AlphaGo is a beginning.”

My personal
experience with Go dates back at least a decade. I remember getting slaughtered
every time by the free GNUgo software, just as I had been by every human
opponent for the last 20 years. Never got the hang of it, though I was school
chess captain back in the day. Totally different mindset. I first came across
it in a little-remembered crime series called 'The Man in Room 17', with
Richard Vernon and Denholm Ellit eponymously solving crimes without leaving
their office, where they were always playing go. I also remember a funny little
story while I was attending the British Council. Back in the 80s, a Korean guy
gave me a game. After every move I played, he stifled a laugh and started a
rapid fire of, "No! Cos you purrin ['put in', I presume] there, then I
purrin here, after you purrin there an' I purrin here, you lose these
piece" None of which made anything clearer. At chess, the first (okay,
tenth) time I got mated on the back row by a rook, I learned not to leave the
king behind a wall of pawns. Never got my head round the simplest 'joseki'
(corner opening) at Go. Beautifully elegant game though.

When reading
about the game in Kasparov’s book, I just got sidetracked. Back in the day,
along with Chess, I tried to develop a Go AI engine. Sad to say, I could never
build it to my full satisfaction; I was able to beat it 9 out of 10 times. Not so with chess. My AI Chess developed in C,
if I may say so, was quite good. Does AlphaGo's success tell us something about
the mindfulness of its technology, or does it instead tell us something about
the mindlessness of games like chess and Go? Back in the day I studied AphaGo's
performance, and impressed though I was by its playing strength, I did notice
that it seemed to not understand two basic concepts of Go called
"sente" (seizing the initiative) and "aji" (leaving a rock
in the road for the opponent to trip over later), as was evidenced by
opportunities it missed. What is quite remarkable is that AlphaGo doesn't
understand a single thing about Go, except how to count the final score!
AlphaGo circumvents the problem of understanding the toy world of Go by using
two mathematical tricks: (1) learning knee-jerk reactions and (2) statistically
sensible guesswork. A knee-jerk reaction is an automatic reaction to an event
that seems to match a pattern; we rely upon such reactions to avoid dangers
such as the edge of a cliff or a fire. Such reactions are essential for
survival, but they are also unreliable because what we think we see is not
always what is there. A pretty face does not necessarily imply a pretty mind.
Anyone who has used Google's search engine will know that whereas it is superb
at finding information, it is also somewhat clueless as it pulls up a
wastepaper basketful of irrelevant snow as well as the one or two nuggets you
were looking for. Because Google doesn't speak English. It knows nothing about
the world we live in so relies instead solely upon statistical pattern matching
to find its answers, much the same as IBM's Jeopardy champ "Watson"
does. Jeopardy and document search are tasks well-suited to mindless
association-seeking. AlphaGo is of the same breed as Google search and Watson;
there are nuances of difference in their pattern-matching algorithms, but the
underlying principle is the same: they all search for matching patterns,
without troubling to understand what the patterns mean in terms of an ontology
of cause and effect. In AlphaGo's case, the patterns it looks for are ones it
has inferred by using an artificial intelligence technology called an
"(artificial) neural network" that has had some success in learning
to recognise a specific object in photographs - most famously, whether there is
a cat in a YouTube video.

A Go game in
progress is nothing more complicated than a very simple digital photograph,
made of just 19x19 pixels, each of which can have just one of three colours:
black, white, or empty. So people thought that what works for seeing cats in
videos might also work for seeing good moves in Go.

And it does.

In convolutions
of artificial neurons, information flows both ways through a stratified
network. They are capable of learning patterns more complex than simple one-way
networks - although perhaps it would be better to say that they can learn
probable patterns, since the mathematics they use creates a probability
spectrum of possible identifications. And that is just what's needed to play Go
against people, for not even a Go grand-master can say unequivocally what is the
best move in the middle of a game. AlphaGo's neural network was trained by
showing it what good players did in over 30 million positions taken from a
database of expert-level games. It produces a spectrum of knee-jerk reaction
good move possibilities, but it doesn't stop there. It goes on to imagine what
might happen in the future. AlphaGo's future-guessing methods are different
from those used by Deep Blue to defeat chess champ Gary Kasparov, but both
their methods are essentially brute-force techniques, relying on sampling
millions of possible sequences rather than examining a few pertinent lines by
goal-directed knowledge-based search.

AlphaGo can do
one thing that Deep Blue could not: it can learn. Right now, it is learning to
improve its stockpile of patterns by playing itself every day and teaching
itself which moves worked out well during those experiments. However, the rate
of improvement of a convolutional neural network reduces over time, so there is
every reason to doubt that AlphaGo will become strong enough to beat Lee Sedol.

Nevertheless,
both Deep Blue and AlphaGo have reached a game-playing ability higher than 99%
of those of us who have also tried to play chess or Go, so we humans should
perhaps hang our heads in shame at being so incompetent at reasoning that an
unreasoning machine can better us at games we thought to be intellectual
challenges requiring sophisticated strategy and tactics! However, although
computers can now beat us at board games as well as see a cat in a video, we
need not fear that they are about to take over the world and turn us into their
domesticated animals. The 0.1% have already won that game.

NB: I closely
followed the match between Deep Blue and Kasparov at the time. The 6th
(last) game was especially unfathomable. I remember thinking how could Kasparov
play into a well-known opening trap in the Caro-Kann. WTF? When a world
champion plays like a beginner, there is not much to be said, and much to be
sad about. It wasn’t that Deep Blue “outmaneuvered” Kasparov, it was that
Kasparov defeated himself. My disenchantment with chess started with this
specific game. This match was a travesty and I never recovered from it.

4 comentários:

My only contact with "Go" is through the manga "Hikaru no go" that I read back in '08. I tried to play a couple of games on some freeware and lost so badly that I never tried again. It is such a different mindset, as you noted, that I just can't grasp the necessary strategy.

"so we humans should perhaps hang our heads in shame at being so incompetent at reasoning that an unreasoning machine can better us at games we thought to be intellectual challenges requiring sophisticated strategy and tactics"

Well, I'd like to ask that ai system to ride a bike. Or color a picture within the lines. It can't. It wasn't designed to. Humans haven't been designed to all excel at the same thing. But we can do about eleventy billion DIFFERENT things. Those machines are precision instruments meant to do one thing extremely well. Now, I'll be worried when cyborgs can start having physical presence in titanium shells. Or when cars, planes, etc start thinking for themselves.

However, I always think they'll fail eventually. Because humans are flawed, their work is flawed and their assumptions and their biases, etc are all flawed. Whatever they do will be flawed as well. Now, that doesn't mean something won't destroy us all WITH its flaws, lol...

Most of our beloved jobs and so-called careers were redundant in the first place. AI not only shines a light on our own futility, but will also allow us to bask in what we truly relish as a species: self-absorbed naval gazing. Which is ok, because technology will soon be able to provide the food, clothing, and shelter for which we worry and work so hard for, but at minimal or almost free cost. The paradigm shift swings upon us--bring on Universal Basic Income! Certainly an exciting time for humanity.

What's the point of the rat race if robots are doing all the work anyway? Universal income, humane population management, aggressive push to colonize the rest of the solar system and beyond...and of course the scrapping of the whole "life=work=money" notion that the wealthy few use to enslave the rest of us billions.

I must say that Im begining to agree with a vision of complementary humans to excendentary robots. Everywhere you go you see machinery taking human jobs, not because they are cheaper but because they can't find the people to work doing that jobs...Check agricultural crops nowadays. Even grape crops are machines I heard in reguengos de monsaraz where i was last weekend.The emigration rate was so high in the last 5 years that the crops had to be done by machines!

Luis, that's a very interesting piece there; let's hope that it does not continue to be the case. It is clear that some areas will suffer though, and the creation of different types of work will be essential. One day it'll all be in the history books...